UWSNs multi-hop routing method based on AUV networking

Abstract

The invention discloses a UWSNs multi-hop routing method based on AUV networking. The UWSNs multi-hop routing method comprises the following steps: establishing a first virtual pipeline, a second virtual pipeline and a third virtual pipeline; based on the first virtual pipeline, using an offline reinforcement learning method to obtain a first optimal relay node; based on the second virtual pipeline, obtaining a second optimal relay node by using an offline reinforcement learning method and an online reinforcement learning method; based on a third virtual pipeline, a storage-carrying-forwarding mechanism is adopted, and data collected by the AUV for multiple times are forwarded to a third optimal relay node at the same time; and forwarding the collected data to the first Sink node, the second Sink node and the third Sink node through the first optimal relay node, the second optimal relay node and the third optimal relay node to complete a data forwarding task. The invention provides a routing protocol which effectively solves the problems of low data delivery rate, high energy consumption and short network life in UWSNs.

Description

technical field [0001] The present application belongs to the technical field of network communication, and in particular relates to a UWSNs multi-hop routing method based on AUV networking. Background technique [0002] my country is rich in marine resources such as offshore oil and natural gas resources, tidal energy / wind energy resources, fishery resources, etc. It is of high economic value and strategic significance to effectively develop and protect marine resources and maintain marine safety. [0003] In many emerging marine science and technology fields, underwater sensor networks have received increasing attention due to their applications in water pollution monitoring, underwater resource exploration, marine geographic and meteorological data collection, and earthquake / tsunami disaster prevention in recent years. For example, in terms of marine geographic data collection, the UWSNs network uses sensors deployed on the surface and underwater to collaboratively collec...

AI Technical Summary

Problems solved by technology

In the Q-learning-based adaptive routing protocol (QELAR), the reward function is defined according to the remaining energy of the sensor nodes, and the network lifetime is extended by selecting the next-hop node with more remaining energy, but each node needs to exchange metadata packets Learn the surrounding network environment to determine the forwarding node of the next hop, which will cause each node to consume more energy; in addition, QELAR does not strictly control the network end-to-end delay when selecting the next hop node with more energy; moreover , the routing algorithm that relies on immediate rewards will make the path selection fall into a local optimum, not a global optimum

In the same delay-sensitive QDAR routing protocol based on Q-learning, by defining a behavior-utility function that comprehensively considers the residual energy and propagation delay, the route is adaptively selected to prolong the network life, but the source node must send data packets to the network in advance. Broadcast preparation information, the surface receiver receives the information and makes path planning, then passes the routing information to the source node along the planned path, the source node then sends the data packet, and the selected forwarding node transmits the data packet, this process Will consume a lot of energy in a network of underwater acoustic sensors with long delays

[0006] For the research of UWSNs on detecting underwater information with AUV as the source node and transmitting the data to the sink node, there is no routing protocol that effectively solves the problem of low data delivery rate, high energy consumption, and short network life in UWSNs, so researchers need to pay attention And committed to research in this area, in order to establish a more efficient and low-consumption underwater wireless sensor network

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